Stability Analysis of Neural Networks
Stability Analysis of Neural Networks

Author: Grienggrai Rajchakit

Publisher: Springer Nature

Published: 2021-12-05

Total Pages: 415

ISBN-13: 9811665346

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This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.

Stability Analysis and State Estimation of Memristive Neural Networks
Stability Analysis and State Estimation of Memristive Neural Networks

Author: Hongjian Liu

Publisher: CRC Press

Published: 2024-10-07

Total Pages: 0

ISBN-13: 9781032038100

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This book discusses the stability analysis and estimator design problems for discrete-time memristive neural networks subject to time-delays and approaches state estimation from different perspectives. Each chapter includes analysis problems and application of theories and techniques to pertinent research areas.

Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control

Author: S.S. Ge

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 296

ISBN-13: 1475765770

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Recent years have seen a rapid development of neural network control tech niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications.

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Author: Jinkun Liu

Publisher: Springer Science & Business Media

Published: 2013-01-26

Total Pages: 375

ISBN-13: 3642348165

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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.

Finite-Time Stability: An Input-Output Approach
Finite-Time Stability: An Input-Output Approach

Author: Francesco Amato

Publisher: John Wiley & Sons

Published: 2018-10-08

Total Pages: 184

ISBN-13: 1119140528

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Systematically presents the input-output finite-time stability (IO-FTS) analysis of dynamical systems, covering issues of analysis, design and robustness The interest in finite-time control has continuously grown in the last fifteen years. This book systematically presents the input-output finite-time stability (IO-FTS) analysis of dynamical systems, with specific reference to linear time-varying systems and hybrid systems. It discusses analysis, design and robustness issues, and includes applications to real world engineering problems. While classical FTS has an important theoretical significance, IO-FTS is a more practical concept, which is more suitable for real engineering applications, the goal of the research on this topic in the coming years. Key features: Includes applications to real world engineering problems. Input-output finite-time stability (IO-FTS) is a practical concept, useful to study the behavior of a dynamical system within a finite interval of time. Computationally tractable conditions are provided that render the technique applicable to time-invariant as well as time varying and impulsive (i.e. switching) systems. The LMIs formulation allows mixing the IO-FTS approach with existing control techniques (e. g. H∞ control, optimal control, pole placement, etc.). This book is essential reading for university researchers as well as post-graduate engineers practicing in the field of robust process control in research centers and industries. Topics dealt with in the book could also be taught at the level of advanced control courses for graduate students in the department of electrical and computer engineering, mechanical engineering, aeronautics and astronautics, and applied mathematics.

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation

Author: Igor V. Tetko

Publisher: Springer Nature

Published: 2019-09-09

Total Pages: 848

ISBN-13: 3030304876

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The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Recurrent Neural Networks for Prediction
Recurrent Neural Networks for Prediction

Author: Danilo P. Mandic

Publisher:

Published: 2001

Total Pages: 318

ISBN-13:

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Neural networks consist of interconnected groups of neurons which function as processing units. Through the application of neural networks, the capabilities of conventional digital signal processing techniques can be significantly enhanced.

Qualitative Analysis and Synthesis of Recurrent Neural Networks
Qualitative Analysis and Synthesis of Recurrent Neural Networks

Author: Anthony Michel

Publisher: CRC Press

Published: 2001-12-04

Total Pages: 508

ISBN-13: 9780824707675

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"Analyzes the behavior, design, and implementation of artificial recurrent neural networks. Offers methods of synthesis for associative memories. Evaluates the qualitative properties and limitations of neural networks. Contains practical applications for optimal system performance."

Complex-valued Neural Networks
Complex-valued Neural Networks

Author: Akira Hirose

Publisher: World Scientific

Published: 2003

Total Pages: 387

ISBN-13: 9812384642

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In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

Neural Network Control of Nonlinear Discrete-Time Systems
Neural Network Control of Nonlinear Discrete-Time Systems

Author: Jagannathan Sarangapani

Publisher: CRC Press

Published: 2018-10-03

Total Pages: 624

ISBN-13: 1420015451

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Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.